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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

共 4468 条记录，以下是91-100 订阅

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Early Detection of Road Traffic Injuries with IoT sensor Networks and K-Nearest Neighbors Algorithm

Early Detection of Road Traffic Injuries with IoT Sensor Net...

引用

Visual Analytics and data visualization (ICVADV), International conference on

作者： L.N. Jayanthi Kishore Verma S R. Usha G. Sekar V.T. Lakshmi N. Mohankumar Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus (GNITC) Hyderabad Telangana India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering VSB College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed To Be University) Salem Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Injuries sustained in road accidents are a major worldwide public health concern, highlighting the need for effective early detection systems. By combining the K-Nearest Neighbors (KNN) algorithm with Internet of Things (IoT) sensor networks, this research provides a novel approach to the early identification of road traffic injuries. Critical characteristics like vehicle speed, acceleration, and closeness may be continually monitored in real-time by installing IoT sensors in vehicles and road infrastructure. The data is further processed using the KNN algorithm, which may detect trends that might point to the incidence of traffic injuries. This preventative method allows for prompt action, which may lessen the impact of injuries and save lives. One of the many benefits of the proposed system is its quick detection and reaction time, which helps improve road safety and public health. Results from experimental tests show that the suggested method may reliably identify and forecast injuries sustained in traffic accidents. The significance of using IoT technology and machine learning algorithms for preventing and mitigating injuries in transportation systems is highlighted in this study, which shows the evolution of road safety early warning systems.

关键词： Machine learning algorithms Nearest neighbor methods Road safety Real-time systems Road traffic Classification algorithms Internet of Things Public healthcare Injuries Testing

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Smart Water Management System For Rural And Urban Infrastructure Using Iot

Smart Water Management System For Rural And Urban Infrastruc...

引用

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： M Joshika R Ashwin S Hema Varshini A K Jubair Ahamed B Sakthikumar Department of ECE Sri Eshwar College of Engineering Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The IoT-based Smart Water Management System represents modern state-of-the-art technology which provides effective control of water resources for urban and rural areas. This system enables real-time assessment of water levels as well as stream flow and quality and usage pattern data by using advanced sensors and actuators and Internet of Things devices. The water delivery system benefits from precise wastage reduction through NodeMCU processing of data acquired from Ultrasonic sensor and flow sensor inputs that monitor water levels and leak detection. Motor pumps and valves receive data through the Driver Circuit. Cookies send the data to cloud infrastructure to display vital information through web-based dashboards for remote monitoring purposes. The system employs machine learning algorithms for detecting anomalies while also predicting future water needs to adjust the water distribution. The system uses Java's animation function to display water tank levels chaotically which provides both improved visual interface and instant system performance monitoring. The method saves 30% of water resources while reducing human labor expenses and ensures more sustainable water management and energy-efficient solutions. The proposed plan shows successful implementation for eco-conscious water resource management and environmental preservation within industrial spaces and agricultural zones and smart urban areas.

关键词： Water Cloud computing visualization Machine learning algorithms Valves sensor systems Real-time systems Internet of Things Leak detection Water resources

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Research on Multi sensor Neural Network Decision and Prediction System to Cloud Platform 25

Research on Multi Sensor Neural Network Decision and Predict...

引用

Proceedings of the 2025 3rd International conference on Communication Networks and Machine Learning

作者： Peng Peng Jiamin Cao Xiaofei Xu Wenyi Zhang Haotian Liu Xuanlin Meng Jun Wang School of Automation Beijing University of Information Technology Beijing China Department of Communication Engineering Communication University of China Beijing China

ISBN: (纸本)9798400713231

With the comfort of home environment, the demand for early warning capability of safety accidents is growing day by day. Through real-time collection of a large amount of data for intelligent decision-making analysis and research, a set of intelligent decision-making system for home environment with optimal comfort of popular indoor environment based on Multi sensor Neural Network Prediction is designed. The method in this paper is to use Alibaba Cloud's IoT Studio project console to build interactive Web visualization pages such as "real-time environment status information", use fuzzy fusion Bayesian decision model as system algorithm condition judgment, realize mobile phone push of alarm function of home environment intelligent decision system on Multi sensor Neural Network Prediction, and develop neural network temperature prediction function.

关键词： Bayesian decision

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Classification of Vehicle Classes at Toll Gates Using YOLOv9 and Resnet50

Classification of Vehicle Classes at Toll Gates Using YOLOv9...

引用

Advancement in data Science, E-learning and Information Systems (ICADEIS), International conference on

作者： Abdul Aziz Febriyansah Gamma Kosala School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Indonesia's current toll road payment system uses Automatic Vehicle Classification (AVC) technology, which relies on various sensors such as induction sensors, LiDAR, and radar. This approach incurs high operational costs. For this reason, more efficiency is required. Toll roads are vital infrastructure supporting vehicle mobility in Indonesia, and operational efficiency directly impacts overall performance. The existing AVC technology entails high costs and often faces technical challenges, necessitating a solution that enhances efficiency in terms of operational costs, accuracy, and system maintenance. This research combines the You Only Look Once (YOLO) method for detecting vehicle axles, represented by tires, and Residual Network (ResNet) for classifying vehicle categories based on YOLO's detection results. The dataset comprises 1,196 vehicle images across five distinct classes. The proposed system aims to improve accuracy, cost, and system maintenance efficiency. The findings indicate that the system achieves a classification accuracy of 97%, providing a practical solution that contributes to the advancement of toll gate vehicle classification technology with lower operational costs and higher accuracy.

关键词： YOLO Costs Accuracy Roads Logic gates sensor systems sensors Maintenance Residual neural networks Axles

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Pipeline Defect Detection Based on Matrix-Array Solid-State LiDAR

Pipeline Defect Detection Based on Matrix-Array Solid-State ...

引用

IEEE International conference on Industrial technology (ICIT)

作者： Qingyu Huang Zhihua Yu Xiaoyan Yang Yuxuan Xiong Hui Li Dunying Yu School of Automation China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

To accurately detect surface defects in pipelines and obtain the depth and size information of the defect locations, this paper proposes a 3D point cloud detection method based on a matrix-array solid-state LiDAR. First, the collected point clouds undergo statistical filtering and voxel grid downsampling to remove noise and redundant data. Then, the Random Sample Consensus (RANSAC) algorithm is used to fit the standard plane. Next, a coarse registration is performed using the Sample Consensus (SAC) algorithm combined with the Fast Point Feature Histogram (FPFH), followed by fine registration with the Adaptive Iterative Closest Point (ICP) algorithm to achieve high-precision alignment between the defect point cloud and the reference point cloud. Finally, a region growing based clustering and segmentation method is used to extract the defect point cloud, enabling the quantification and visualization of the defect location. Experimental results demonstrate that this method can accurately extract pipeline defects and measure the depth and dimensions of the defect locations.

关键词： Point cloud compression Laser radar Three-dimensional displays Filtering Pipelines Clustering algorithms Robustness Root mean square Standards Defect detection

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Ultra-High-Frequency Harmony: mmWave radar and Event Camera Orchestrate Accurate Drone Landing 25

Ultra-High-Frequency Harmony: mmWave Radar and Event Camera ...

引用

Proceedings of the 23rd ACM conference on Embedded Networked sensor Systems

作者： Haoyang Wang Jingao Xu Xinyu Luo Xuecheng Chen Ting Zhang Ruiyang Duan Yunhao Liu Xinlei Chen Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Carnegie Mellon University Pittsburgh USA Tsinghua University Shenzhen Guangdong China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong China Meituan Technology Shenzhen Guangdong China Tsinghua University beijing China Pengcheng Laboratory Shenzhen Guangdong China RISC-V International Open Source Laboratory Shenzhen Guangdong China

ISBN: (纸本)9798400714795

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

关键词： drone ground localization

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Design and Validation of Learning Aware HMI for Learning-Enabled Increasingly Autonomous Systems

Design and Validation of Learning Aware HMI for Learning-Ena...

引用

Annual IEEE Systems conference

作者： Parth Ganeriwala Michael Matessa Siddhartha Bhattacharyya Randolph M. Jones Jennifer Davis Parneet Kaur Simone Fulvio Rollini Natasha Neogi Computer Engineering and Sciences Florida Institute of Technology Melbourne FL USA Applied Research and Technology Collins Aerospace Cedar Rapids IA USA Applied Cognitive Systems Soar Technology Ann Arbor MI USA Applied Research and Technology Collins Aerospace Cedar Rapids IA U.S.A. Applied Research and Technology Collins Aerospace Rome Italy Langley Research Center NASA Hampton VA USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

关键词： Laser radar Human-machine systems NASA Collaboration Reinforcement learning Safety Reliability Logic Artificial intelligence Standards

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Improved Extrinsic Calibration of Acoustic Cameras via Batch Optimization

Improved Extrinsic Calibration of Acoustic Cameras via Batch...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhi Li Jiang Wang Xiaoyang Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems and the Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acoustic cameras have found many applications in practice. Accurate and reliable extrinsic calibration of the microphone array and visual sensors within acoustic cameras is crucial for fusing visual and auditory measurements. Existing calibration methods either require prior knowledge of the microphone array geometry or rely on grid search which suffers from slow iteration speed or poor convergence. To overcome these limitations, in this paper, we propose an automatic calibration technique using a calibration board with both visual and acoustic markers to identify each microphone position in the camera frame. We formulate the extrinsic calibration problem (between microphones and the visual sensor) as a nonlinear least squares problem and employ a batch optimization strategy to solve the associated problem. Extensive numerical simulations and real-world experiments show that the proposed method improves both the accuracy and robustness of extrinsic parameter calibration for acoustic cameras, in comparison to existing methods. To benefit the community, we open-source all the codes and data at https://***/AISLAB-sustech/AcousticCamera.

关键词： visualization Accuracy Cameras Numerical simulation Acoustics Robustness Microphone arrays Calibration Optimization sensor arrays

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Comprehensive Review of Cybersecurity Vulnerabilities and Safety Concerns in Autonomous Vehicles

Comprehensive Review of Cybersecurity Vulnerabilities and Sa...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Uppalapati Sudheer Kumar Sukanta Halder Basant Kumar Sethi Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Electrical Engineering Indian Institute of Technology(ISM) dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machine learning for threat detection, and robust encryption methods.

关键词： Image sensors radar imaging Threat assessment sensor systems Safety sensors Encryption Security Computer security Autonomous vehicles

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Fusion of IoT and GPS Technologies for Enhancing Security of ATM Cash Refilling Van

Fusion of IoT and GPS Technologies for Enhancing Security of...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： B. Gopinath A. Vijay Karthick K. Srinivasan Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Monitoring an ATM cash refilling van using Global Positioning System (GPS) tracker and Internet of Things (IoT) technology is proposed in this work. It involves real-time tracking and efficient management of cash refilling processes by the integration of GPS tracker and IoT technology with appropriate sensors to ensure security. It provides real-time supervised operation throughout the journey of ATM cash refilling van from the starting point to the destination point. The tracking system uses various IoT sensors that are connected to ESP32C3 microcontroller unit, an SOS (Save Our Souls / Save Our Ship) button and a mobile phone device. The ESP32C3 microcontroller unit generates necessary control signals with Wi-Fi data transmission. These devices continuously collect the data for further transmission to a cloud-based ThingSpeak IoT visualization platform, which explores the information to the stakeholders involved in the ATM cash refilling process. Key features of this system include real-time location tracking, temperature and humidity monitoring, vehicle door open-close security alerts, and data analytics capabilities. Furthermore, historical data collected by the system can be used to improve the security of the ATM cash refilling van. In conclusion, the IoT-based GPS tracker is a powerful tool that ensures security, real-time visibility, and data-driven insights for businesses involved in the transportation of ATM cash.

关键词： Automated teller machine Transportation Containers Real-time systems sensor systems Security Optical sensors Internet of Things Monitoring Global Positioning System

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